Exploratory Data Analysis of Movies

import pandas as pd
pd.options.mode.chained_assignment = None
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
import os

import plotly.express as px

starting with: https://www.kaggle.com/somyamaheshwari/data-analysis-and-visualization-plotly

os.listdir()

['MoviesOnStreamingPlatforms_updated.csv',
 '.jovianrc',
 '.ipynb_checkpoints',
 'Screen Shot 2020-10-20 at 7.05.03 PM.png',
 'streaming-movies.ipynb']

raw_movies_df = pd.read_csv('MoviesOnStreamingPlatforms_updated.csv', index_col=1)

raw_movies_df.drop(columns='Unnamed: 0', inplace=True)

raw_movies_df.head()

	Title	Year	Age	IMDb	Rotten Tomatoes	Netflix	Hulu	Prime Video	Disney+	Type	Directors	Genres	Country	Language	Runtime
ID
1	Inception	2010	13+	8.8	87%	1	0	0	0	0	Christopher Nolan	Action,Adventure,Sci-Fi,Thriller	United States,United Kingdom	English,Japanese,French	148.0
2	The Matrix	1999	18+	8.7	87%	1	0	0	0	0	Lana Wachowski,Lilly Wachowski	Action,Sci-Fi	United States	English	136.0
3	Avengers: Infinity War	2018	13+	8.5	84%	1	0	0	0	0	Anthony Russo,Joe Russo	Action,Adventure,Sci-Fi	United States	English	149.0
4	Back to the Future	1985	7+	8.5	96%	1	0	0	0	0	Robert Zemeckis	Adventure,Comedy,Sci-Fi	United States	English	116.0
5	The Good, the Bad and the Ugly	1966	18+	8.8	97%	1	0	1	0	0	Sergio Leone	Western	Italy,Spain,West Germany	Italian	161.0

Make the column names uniform

raw_movies_df.columns

Index(['Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Netflix', 'Hulu',
       'Prime Video', 'Disney+', 'Type', 'Directors', 'Genres', 'Country',
       'Language', 'Runtime'],
      dtype='object')

EX) Rename columns: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rename.html df.rename(columns={"A": "a", "B": "c"})

raw_movies_df.columns = map(str.lower, raw_movies_df.columns)

Making all column names lower case for ease of use. Can also use: df.rename(str.lower, axis='columns')

raw_movies_df = raw_movies_df.rename(columns={'rotten tomatoes':'rotten_tomatoes','prime video': 'prime_video'})

raw_movies_df

	title	year	age	imdb	rotten_tomatoes	netflix	hulu	prime_video	disney+	type	directors	genres	country	language	runtime
ID
1	Inception	2010	13+	8.8	87%	1	0	0	0	0	Christopher Nolan	Action,Adventure,Sci-Fi,Thriller	United States,United Kingdom	English,Japanese,French	148.0
2	The Matrix	1999	18+	8.7	87%	1	0	0	0	0	Lana Wachowski,Lilly Wachowski	Action,Sci-Fi	United States	English	136.0
3	Avengers: Infinity War	2018	13+	8.5	84%	1	0	0	0	0	Anthony Russo,Joe Russo	Action,Adventure,Sci-Fi	United States	English	149.0
4	Back to the Future	1985	7+	8.5	96%	1	0	0	0	0	Robert Zemeckis	Adventure,Comedy,Sci-Fi	United States	English	116.0
5	The Good, the Bad and the Ugly	1966	18+	8.8	97%	1	0	1	0	0	Sergio Leone	Western	Italy,Spain,West Germany	Italian	161.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
16740	The Ghosts of Buxley Hall	1980	NaN	6.2	NaN	0	0	0	1	0	Bruce Bilson	Comedy,Family,Fantasy,Horror	United States	English	120.0
16741	The Poof Point	2001	7+	4.7	NaN	0	0	0	1	0	Neal Israel	Comedy,Family,Sci-Fi	United States	English	90.0
16742	Sharks of Lost Island	2013	NaN	5.7	NaN	0	0	0	1	0	Neil Gelinas	Documentary	United States	English	NaN
16743	Man Among Cheetahs	2017	NaN	6.6	NaN	0	0	0	1	0	Richard Slater-Jones	Documentary	United States	English	NaN
16744	In Beaver Valley	1950	NaN	NaN	NaN	0	0	0	1	0	James Algar	Documentary,Short,Family	United States	English	32.0

16744 rows × 15 columns

raw_movies_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 16744 entries, 1 to 16744
Data columns (total 15 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   title            16744 non-null  object 
 1   year             16744 non-null  int64  
 2   age              7354 non-null   object 
 3   imdb             16173 non-null  float64
 4   rotten_tomatoes  5158 non-null   object 
 5   netflix          16744 non-null  int64  
 6   hulu             16744 non-null  int64  
 7   prime_video      16744 non-null  int64  
 8   disney+          16744 non-null  int64  
 9   type             16744 non-null  int64  
 10  directors        16018 non-null  object 
 11  genres           16469 non-null  object 
 12  country          16309 non-null  object 
 13  language         16145 non-null  object 
 14  runtime          16152 non-null  float64
dtypes: float64(2), int64(6), object(7)
memory usage: 2.0+ MB

raw_movies_df['rotten_tomatoes'] = raw_movies_df['rotten_tomatoes'].str.replace('%', '')

raw_movies_df.head()

	title	year	age	imdb	rotten_tomatoes	netflix	hulu	prime_video	disney+	type	directors	genres	country	language	runtime
ID
1	Inception	2010	13+	8.8	87	1	0	0	0	0	Christopher Nolan	Action,Adventure,Sci-Fi,Thriller	United States,United Kingdom	English,Japanese,French	148.0
2	The Matrix	1999	18+	8.7	87	1	0	0	0	0	Lana Wachowski,Lilly Wachowski	Action,Sci-Fi	United States	English	136.0
3	Avengers: Infinity War	2018	13+	8.5	84	1	0	0	0	0	Anthony Russo,Joe Russo	Action,Adventure,Sci-Fi	United States	English	149.0
4	Back to the Future	1985	7+	8.5	96	1	0	0	0	0	Robert Zemeckis	Adventure,Comedy,Sci-Fi	United States	English	116.0
5	The Good, the Bad and the Ugly	1966	18+	8.8	97	1	0	1	0	0	Sergio Leone	Western	Italy,Spain,West Germany	Italian	161.0

#converting to number:

#run as float first!
raw_movies_df['rotten_tomatoes'] = raw_movies_df['rotten_tomatoes'].astype('float')

#raw_movies_df['rotten_tomatoes'] = raw_movies_df['rotten_tomatoes'].astype('int64')
#"int64" will not work because of the NaN values, but "Int64" does. 

raw_movies_df['rotten_tomatoes'] = raw_movies_df['rotten_tomatoes'].astype('Int64')

raw_movies_df['rotten_tomatoes']

ID
1          87
2          87
3          84
4          96
5          97
         ... 
16740    <NA>
16741    <NA>
16742    <NA>
16743    <NA>
16744    <NA>
Name: rotten_tomatoes, Length: 16744, dtype: Int64

raw_movies_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 16744 entries, 1 to 16744
Data columns (total 15 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   title            16744 non-null  object 
 1   year             16744 non-null  int64  
 2   age              7354 non-null   object 
 3   imdb             16173 non-null  float64
 4   rotten_tomatoes  5158 non-null   Int64  
 5   netflix          16744 non-null  int64  
 6   hulu             16744 non-null  int64  
 7   prime_video      16744 non-null  int64  
 8   disney+          16744 non-null  int64  
 9   type             16744 non-null  int64  
 10  directors        16018 non-null  object 
 11  genres           16469 non-null  object 
 12  country          16309 non-null  object 
 13  language         16145 non-null  object 
 14  runtime          16152 non-null  float64
dtypes: Int64(1), float64(2), int64(6), object(6)
memory usage: 2.1+ MB

Now for the dataset!

movie_df = raw_movies_df.copy()

movie_df.describe()

	year	imdb	rotten_tomatoes	netflix	hulu	prime_video	disney+	type	runtime
count	16744.000000	16173.000000	5158.000000	16744.000000	16744.000000	16744.000000	16744.000000	16744.0	16152.000000
mean	2003.014035	5.902751	65.428461	0.212613	0.053930	0.737817	0.033684	0.0	93.413447
std	20.674321	1.347867	26.614496	0.409169	0.225886	0.439835	0.180419	0.0	28.219222
min	1902.000000	0.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.0	1.000000
25%	2000.000000	5.100000	44.000000	0.000000	0.000000	0.000000	0.000000	0.0	82.000000
50%	2012.000000	6.100000	71.000000	0.000000	0.000000	1.000000	0.000000	0.0	92.000000
75%	2016.000000	6.900000	88.000000	0.000000	0.000000	1.000000	0.000000	0.0	104.000000
max	2020.000000	9.300000	100.000000	1.000000	1.000000	1.000000	1.000000	0.0	1256.000000

#missing data

total = movie_df.isnull().sum().sort_values(ascending=False)
percent = (movie_df.isnull().sum()/movie_df.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
missing_data

	Total	Percent
rotten_tomatoes	11586	0.691949
age	9390	0.560798
directors	726	0.043359
language	599	0.035774
runtime	592	0.035356
imdb	571	0.034102
country	435	0.025979
genres	275	0.016424
type	0	0.000000
disney+	0	0.000000
prime_video	0	0.000000
hulu	0	0.000000
netflix	0	0.000000
year	0	0.000000
title	0	0.000000

# is there way to do the chart above, more simply?
#they are removing rotten tomatoes b/c most of the data is missing. I want to explore why it is missing.

# of movies per platform, 2 different ways:

#using the same idea as with booleans, True=1, False=0, we can just sum the columns:
# movie_df['Netflix'].sum()

#or
netflix_count = len(movie_df[movie_df['netflix'] == 1].index) #why .index? "The index (row labels) of the DataFrame." additionally it is faster, both do the same thing: https://stackoverflow.com/questions/15943769/how-do-i-get-the-row-count-of-a-pandas-dataframe
hulu_count = len(movie_df[movie_df['hulu'] == 1].index)
prime_count = len(movie_df[movie_df['prime_video'] == 1].index)
disney_count = len(movie_df[movie_df['disney+'] == 1].index)

Making a new DF with these counts:

label = ['Netflix', 'Hulu', 'Prime Video', 'Disney']
count = [netflix_count, hulu_count, prime_count, disney_count]
platform = pd.DataFrame(
    {'platform': label,
    'movie_count': count,
    })
platform

	platform	movie_count
0	Netflix	3560
1	Hulu	903
2	Prime Video	12354
3	Disney	564

fig = px.pie(platform, names='platform', values='movie_count')
fig.update_traces(rotation=45, pull=[0.1,0.03,0.03,0.03,0.03], textinfo='percent+label', title='Movie count per platform')
fig.show()

yearly_count = movie_df.groupby('year')['title'].count().reset_index().rename(columns={'title':"movie_count"})
fig = px.bar(yearly_count, x='year', y="movie_count", color='movie_count', height=600)
fig.show()

# average ratings:

movie_df['genres'].value_counts().head(5)

Drama           1341
Documentary     1229
Comedy          1040
Comedy,Drama     446
Horror           436
Name: genres, dtype: int64

top5_genres = ['Drama','Documentary','Comedy', 'Comedy,Drama','Horror']
table = movie_df.loc[:, ['year', 'genres', 'imdb']]
table['avg_rating'] = table.groupby([table.genres, table.year])['imdb'].transform('mean')
table.drop('imdb', axis=1, inplace=True)
table = table[(table.year > 2009) & (table.year<2020)]
table = table.loc[table['genres'].isin(top5_genres)]
table = table.sort_values('year')

fig = px.bar(table, x='genres', y='avg_rating', animation_frame='year', animation_group='genres', 
             color='genres', hover_name='genres', range_y=[0,10])
fig.update_layout(showlegend=False)
fig.show()

top_movies_imdb = movie_df[movie_df['imdb']>8.5][['title', 'directors', 'rotten_tomatoes', 'imdb']].sort_values(by=['rotten_tomatoes'], ascending=False)
top_movies_imdb

	title	directors	rotten_tomatoes	imdb
ID
4474	Stop Making Sense	Jonathan Demme	100	8.6
4663	Tom Petty and the Heartbreakers: Runnin' Down ...	Peter Bogdanovich	100	8.6
3563	Parasite	Bong Joon Ho	99	8.6
5	The Good, the Bad and the Ugly	Sergio Leone	97	8.8
3562	GoodFellas	Martin Scorsese	96	8.7
...	...	...	...	...
11161	Draw Hard	Jon Nix	<NA>	8.7
11208	Along Recovery	Justin J. Springer	<NA>	8.6
11874	Ruby's Studio: the Feelings Show	Jason Docter,Matt Docter	<NA>	8.8
11911	Second Chance By Betrayal	Terrence Weasel Smith	<NA>	8.7
15176	Shred: The Story of Asher Bradshaw	Kathy Herndl	<NA>	8.8

97 rows × 4 columns

top_movies_rt = movie_df[movie_df['rotten_tomatoes']>90.0][['title', 'directors', 'rotten_tomatoes', 'imdb']].sort_values(by=['rotten_tomatoes'], ascending=False)
top_movies_rt

	title	directors	rotten_tomatoes	imdb
ID
16481	Marvel Rising: Secret Warriors	Alfred Gimeno,Eric Radomski	100	5.2
1332	Attacking the Devil: Harold Evans and the Last...	David Morris,Jacqui Morris	100	8.1
5214	Top Spin	Sara Newens,Mina T. Son	100	6.9
5228	Bolivia	Israel Adrián Caetano	100	7.3
5229	The Story of Louis Pasteur	William Dieterle	100	7.3
...	...	...	...	...
3948	All Square	John Hyams	91	5.8
6111	Werewolf	Freddie Francis	91	6.0
6082	Michael H. – Profession: Director	Yves Montmayeur	91	7.0
5084	Cutter's Way	Ivan Passer	91	6.9
959	Nobody Speak: Trials of the Free Press	Brian Knappenberger	91	6.5

1035 rows × 4 columns

Comparing top imdb to top RT. not much overlap.

top_movies = top_movies_imdb.index & top_movies_rt.index

top_movies

Int64Index([4474, 4663, 3563, 5, 3562, 3561, 4440, 4460, 16214, 16213], dtype='int64', name='ID')

movie_df.head()

	title	year	age	imdb	rotten_tomatoes	netflix	hulu	prime_video	disney+	type	directors	genres	country	language	runtime
ID
1	Inception	2010	13+	8.8	87	1	0	0	0	0	Christopher Nolan	Action,Adventure,Sci-Fi,Thriller	United States,United Kingdom	English,Japanese,French	148.0
2	The Matrix	1999	18+	8.7	87	1	0	0	0	0	Lana Wachowski,Lilly Wachowski	Action,Sci-Fi	United States	English	136.0
3	Avengers: Infinity War	2018	13+	8.5	84	1	0	0	0	0	Anthony Russo,Joe Russo	Action,Adventure,Sci-Fi	United States	English	149.0
4	Back to the Future	1985	7+	8.5	96	1	0	0	0	0	Robert Zemeckis	Adventure,Comedy,Sci-Fi	United States	English	116.0
5	The Good, the Bad and the Ugly	1966	18+	8.8	97	1	0	1	0	0	Sergio Leone	Western	Italy,Spain,West Germany	Italian	161.0

movies_by_country = movie_df.groupby('country')['title'].count().reset_index().sort_values('title',ascending = False).head(10).rename(columns = {'title':'movie_count'})
fig = px.pie(movies_by_country,names='country', values='movie_count')
fig.update_traces(rotation=180, pull=[0.1,0.03,0.03,0.03,0.03],textinfo="percent+label", title='Movie Count per Country')
fig.update_layout(showlegend=False)
fig.show()

def movies_count(platform, count=False):
    if count==False:
        print('Movies in {} are {}'. format(platform, movie_df[platform].sum()))
    else:
        return movie_df[platform].sum()

labels = 'netflix', 'hulu', 'prime_video', 'disney'
size = [movies_count('netflix', count=True), 
        movies_count('hulu', count=True),
        movies_count('prime_video', count=True),
        movies_count('disney+', count=True)]

explode = (0.1, 0.1, 0.1, 0.1)

#plotting
fig1, ax1 = plt.subplots()

ax1.pie(size,
       labels = labels,
       autopct = '%1.1f%%',
       explode = explode,
       shadow = True,
       startangle = 100)

ax1.axis = ('equal')
plt.show()

is the above double counting movies that list 2 countries??, no it doesen't seem so but its lieaving out stuff them.

#runtime

sns.displot(movie_df['runtime']);

sns.displot(movie_df['imdb']);

#sns.distplot(movie_df['rotten_tomatoes']);

#ValueError: cannot convert to 'float64'-dtype NumPy array with missing values. Specify an appropriate 'na_value' for this dtype.

movie_df.imdb.describe()

count    16173.000000
mean         5.902751
std          1.347867
min          0.000000
25%          5.100000
50%          6.100000
75%          6.900000
max          9.300000
Name: imdb, dtype: float64

#language

count_by_language = movie_df.groupby('language')['title'].count().reset_index().sort_values('title', ascending=False).head(10).rename(columns={'title': 'movie_count'})
fig = px.bar(count_by_language, x='language', y='movie_count', color='movie_count', height=600)
fig.show()

plt.figure(figsize=(15,7))
chains=movie_df['language'].value_counts()[:20]
sns.barplot(x=chains, y=chains.index, palette='Set2')
plt.title('Languages most commonly made', size=20, pad=20)
plt.xlabel('counts', size=15);

language

total = len(movie_df.language) percent = (len(movie_df)/movie_df.language.count()) total percent lang_info = pd.concat([total, percent], axis=1, keys=['Total', 'Percent']) lang_info

????

(movie_df['language'] == 'English').sum()

10955

len(movie_df.language)

16744

missing_data.loc[missing_data.index == 'language']

	Total	Percent
language	599	0.035774

turn above into a new table for each with percent each language

#Runtime

top_runtime = movie_df.sort_values('runtime', ascending=False).head(10)
fig = px.bar(top_runtime, x='title', y='runtime', color='runtime', height=600)
fig.show()

top_directors = movie_df.groupby('directors')['title'].count().reset_index().rename(columns={'title': 'movie_count'}).sort_values('movie_count', ascending=False).head(10)
fig = px.bar(top_directors, x='directors', y='movie_count', color='movie_count', height=600)
fig.show()

plt.figure(figsize=(15,7))
chains=movie_df['directors'].value_counts()[:20]
sns.barplot(x=chains, y=chains.index, palette='Set1')
plt.title('Most movies made by director', size=20, pad=20)
plt.xlabel('counts', size=15);

top_genres = movie_df.groupby('genres')['title'].count().reset_index().rename(columns={'title': 'movie_count'}).sort_values('movie_count', ascending=False).head(10)
fig = px.bar(top_genres, x='genres', y='movie_count', color='movie_count', height=600)
fig.show()

plt.figure(figsize=(15,7))
chains=movie_df['genres'].value_counts()[:20]
sns.barplot(x=chains, y=chains.index, palette='Set2')
plt.title('Genre Counts', size=20, pad=20)
plt.xlabel('counts', size=15);

now using https://www.kaggle.com/nikhileshkos/recommended-ott-movies-shows-analysis

movie_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 16744 entries, 1 to 16744
Data columns (total 15 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   title            16744 non-null  object 
 1   year             16744 non-null  int64  
 2   age              7354 non-null   object 
 3   imdb             16173 non-null  float64
 4   rotten_tomatoes  5158 non-null   Int64  
 5   netflix          16744 non-null  int64  
 6   hulu             16744 non-null  int64  
 7   prime_video      16744 non-null  int64  
 8   disney+          16744 non-null  int64  
 9   type             16744 non-null  int64  
 10  directors        16018 non-null  object 
 11  genres           16469 non-null  object 
 12  country          16309 non-null  object 
 13  language         16145 non-null  object 
 14  runtime          16152 non-null  float64
dtypes: Int64(1), float64(2), int64(6), object(6)
memory usage: 2.1+ MB

sns.heatmap(movie_df.isnull())

<AxesSubplot:ylabel='ID'>

movie_df.isnull().sum()

title                  0
year                   0
age                 9390
imdb                 571
rotten_tomatoes    11586
netflix                0
hulu                   0
prime_video            0
disney+                0
type                   0
directors            726
genres               275
country              435
language             599
runtime              592
dtype: int64

print("# Age Null: {}".format(movie_df.age.isnull().sum()))

# Age Null: 9390

movie_df.age.value_counts(), movie_df.age.shape

(18+    3474
 7+     1462
 13+    1255
 all     843
 16+     320
 Name: age, dtype: int64,
 (16744,))

sns.countplot(x='age', data=movie_df)

<AxesSubplot:xlabel='age', ylabel='count'>

sns.countplot(x='netflix', data=movie_df);

sns.pairplot(movie_df) fig=plt.gcf() fig.set_size_inches(20,20)

sns.scatterplot(x='year', y='runtime', hue='age', data=movie_df)
fig=plt.gcf()
fig.set_size_inches(8,8)

sns.displot(movie_df['year'])

<seaborn.axisgrid.FacetGrid at 0x7f93fd91c1d0>

plt.figure(figsize=(15,7))
chains=movie_df['country'].value_counts()[:20]
sns.barplot(x=chains, y=chains.index, palette='Set3')
plt.title('Movies made by country', size=20, pad=20)
plt.xlabel('counts', size=15);

sns.displot(movie_df['imdb'], bins=20);

sns.displot(movie_df['imdb'], bins=50);

sns.displot(movie_df['imdb'], bins=10);

def data_inv(df):
    print('No of Rows: ', df.shape[0])
    print('No of Coloums: ', df.shape[1])
    print('**'*25)
    print('Colums Names: \n', df.columns)
    print('**'*25)
    print('Datatype of Columns: \n', df.dtypes)
    print('**'*25)
    print('Missing Values: ')
    c = df.isnull().sum()
    c = c[c>0]
    print(c)
    print('**'*25)
    print('Missing vaules %age wise:\n')
    print((100*(df.isnull().sum()/len(df.index))))
    print('**'*25)
    print('Pictorial Representation:')
    plt.figure(figsize=(8,6))
    sns.heatmap(df.isnull(), yticklabels=False,cbar=False)
    plt.show()  

data_inv(movie_df)

No of Rows:  16744
No of Coloums:  15
**************************************************
Colums Names: 
 Index(['title', 'year', 'age', 'imdb', 'rotten_tomatoes', 'netflix', 'hulu',
       'prime_video', 'disney+', 'type', 'directors', 'genres', 'country',
       'language', 'runtime'],
      dtype='object')
**************************************************
Datatype of Columns: 
 title               object
year                 int64
age                 object
imdb               float64
rotten_tomatoes      Int64
netflix              int64
hulu                 int64
prime_video          int64
disney+              int64
type                 int64
directors           object
genres              object
country             object
language            object
runtime            float64
dtype: object
**************************************************
Missing Values: 
age                 9390
imdb                 571
rotten_tomatoes    11586
directors            726
genres               275
country              435
language             599
runtime              592
dtype: int64
**************************************************
Missing vaules %age wise:

title               0.000000
year                0.000000
age                56.079790
imdb                3.410177
rotten_tomatoes    69.194935
netflix             0.000000
hulu                0.000000
prime_video         0.000000
disney+             0.000000
type                0.000000
directors           4.335882
genres              1.642379
country             2.597946
language            3.577401
runtime             3.535595
dtype: float64
**************************************************
Pictorial Representation:

dropping all null data rows: movies.dropna(subset=['Directors', 'Genres', 'Country', 'Language', 'Runtime'],inplace=True)

checking for any duplicate data

movies.drop_duplicates(inplace=True)

movie_df['title'].value_counts()

Saleslady                       1
The Cape Town Affair            1
Upside Down                     1
Blackbelt                       1
Adam Ferrara: Funny As Hell     1
                               ..
Ill Manors                      1
The Devil and Father Amorth     1
Return of the Street Fighter    1
Mucize                          1
Lang Tong                       1
Name: title, Length: 16744, dtype: int64

#Writing a function to calculate the movies in different platforms

def movies_count(platform, count=False):
    if count==False:
        print('Movies in {} are {}'. format(platform,movie_df[platform].sum()))
    else:
        return movie_df[platform].sum()

movies_count('netflix')
movies_count('hulu')
movies_count('prime_video')
movies_count('disney+')

Movies in netflix are 3560
Movies in hulu are 903
Movies in prime_video are 12354
Movies in disney+ are 564

Directors: Directors, in this column, some directors are present with a ',' so I'll split the names by ',' & then stack it one after the other for easy analysis. Then I wanted to find the director which has the maximum number of movies, to accomplish this, I have set a threshold (10) & plotted the directors which directed more than 10 movies.

from pandas import Series

s = movie_df['directors'].str.split(',').apply(Series, 1).stack()
s.index = s.index.droplevel(-1)
s.name = 'directors'
del movie_df['directors']
df_directors = movie_df.join(s)

data = df_directors['directors'].value_counts()
threshold = 10
prob = data > threshold
data_new = data.loc[prob]

plt.figure(figsize=(10,10))
data_new.plot(kind='bar')
plt.show()

s = movie_df['genres'].str.split(',').apply(Series, 1).stack()
s.index = s.index.droplevel(-1)
s.name = 'genres'
del movie_df['genres']
df_genres = movie_df.join(s)

df_genres.head()

	title	year	age	imdb	rotten_tomatoes	netflix	hulu	prime_video	disney+	type	country	language	runtime	genres
ID
1	Inception	2010	13+	8.8	87	1	0	0	0	0	United States,United Kingdom	English,Japanese,French	148.0	Action
1	Inception	2010	13+	8.8	87	1	0	0	0	0	United States,United Kingdom	English,Japanese,French	148.0	Adventure
1	Inception	2010	13+	8.8	87	1	0	0	0	0	United States,United Kingdom	English,Japanese,French	148.0	Sci-Fi
1	Inception	2010	13+	8.8	87	1	0	0	0	0	United States,United Kingdom	English,Japanese,French	148.0	Thriller
2	The Matrix	1999	18+	8.7	87	1	0	0	0	0	United States	English	136.0	Action

plt.figure(figsize=(10,10))
sns.countplot(x='genres', data=df_genres)
plt.xticks(rotation=90)
plt.show()

s = movie_df['country'].str.split(',').apply(Series, 1).stack()
s.index = s.index.droplevel(-1)
s.name = 'country'
del movie_df['country']
df_country = movie_df.join(s)

df_country['country'].value_counts()[:10].plot(kind='bar')
plt.show()

plt.figure(figsize=(15,7))
chains=df_country['country'].value_counts()[:20]
sns.barplot(x=chains, y=chains.index, palette='Set3')
plt.title('Movies made by country', size=20, pad=20)
plt.xlabel('counts', size=15);

s = movie_df['language'].str.split(',').apply(Series, 1).stack()
s.index = s.index.droplevel(-1)
s.name = 'language'
del movie_df['language']
df_language = movie_df.join(s)

plt.figure(figsize=(15,7))
chains=df_language['language'].value_counts()[:20]
sns.barplot(x=chains, y=chains.index, palette='Set3')
plt.title('Language', size=20, pad=20)
plt.xlabel('counts', size=15);

plt.figure(figsize=(15,7))
chains=raw_movies_df['language'].value_counts()[:20]
sns.barplot(x=chains, y=chains.index, palette='Set3')
plt.title('Language', size=20, pad=20)
plt.xlabel('counts', size=15);